ABSTRACT
This paper deals with the optimal tuning of the controller for the real automatic voltage regulation (AVR) system of the synchronous generator (SG). For this purpose, firstly, a novel proportional-integral controller with two degrees of freedom and anti-windup protection for application in the AVR system is proposed. Secondly, in order to determine the optimal parameters of such a controller, the objective function which takes into account transient response characteristics, disturbance, and measurement noise rejection capabilities of the AVR system is presented. Furthermore, the adaptive modification of the existing metaheuristic African vultures optimization algorithm (AVOA) is introduced for controller parameters design. Finally, unlike the many existing papers in the available literature which use a simplified model of the AVR system, in this work the simulation model of the AVR system is realized by observing the technical documentation of the excitation system of the 40 MVA SG from a hydropower plant Perucica in Montenegro. The results obtained in this work have proven that the proposed AVR controller has superior performances compared with other frequently used controllers in real power plants, in terms of providing transient response quality of the SG terminal voltage, disturbance rejection, and measurement noise mitigation abilities. Additionally, increased convergence speed and improved criterion function value demonstrated that the proposed adaptive modification of the AVOA algorithm outperforms some of the most popular metaheuristic algorithms. © 2017 Elsevier Inc. All rights reserved.
ABSTRACT
A rule-based control and its application in functional electrical stimulation (FES) assisted walking of subjects with paraplegia are described in this paper. The design of rules for control comprises the following two steps: (1) determination of muscle activation patterns by using a fully customized spatial (3D) model of paraplegic walking, and (2) learning of rules, that is, correlation between the muscle activation patterns and kinematics of walking by means of an artificial neural network. The adopted FES system activated eight muscle groups with surface electrodes. The only joints allowing movement in the coronal plane were the hips, and externally controlled joints in sagittal plane were ankles, knees and hips. The simulation minimized the tracking error of the joint angles and the total activation of all eight muscles being stimulated. A radial-basis function artificial neural network was applied for learning of rules. Three automatically controlled modes (slow, near-normal, and near-ballistic) and hand-controlled walking were evaluated in six subjects with a complete spinal cord lesion (T8-T10). The performance of walking was assessed by the following: (1) energy consumption based on oxygen uptake, (2) physiological cost index, (3) maximum speed of walking, and (4) a questionnaire. The results showed that all modes of walking are achievable and that automatic control leads to more efficient and faster walking. The speed of walking achieved by automatic control was almost three times bigger compared with the speed of hand-controlled walking. The energy cost and rate decreased significantly when automatic control was applied; yet, they were still much bigger than the values measured in able-bodied subjects. The objective outcome measures suggest that the near-ballistic walking was the most effective, yet a questionnaire shows that most subjects preferred slow walking. The most likely reason for the preference of lower efficiency walking over the faster end energy efficient near-ballistic walking was that paraplegic patients had difficulties in synchronizing the voluntary movement of the trunk and arms to the artificially controlled movements of legs.
